Corn stover is an abundant, low-cost, and representative lignocellulosic agricultural residue for bioethanol production, but its recalcitrant structure requires effective pretreatment to improve downstream conversion performance. Ball milling technology has been widely used to pretreat lignocellulosic biomass, where regulating particle size distribution enhances lignin utilization and subsequent ethanol production. However, systematic optimization of ball milling operating parameters remains insufficiently investigated. To bridge the gap between bench-scale experimentation and process-level performance prediction, this study establishes an integrated experimental and simulation framework. Ball milling experiments are conducted to systematically vary key operating parameters, and particle morphology is precisely quantified through optical imaging coupled with digital image analysis. Empirical correlations between these operating conditions and particle size characteristics are derived through polynomial fitting. These correlations are then embedded into an Aspen Plus process model for bioconversion performance evaluation. Evaluation from this integrated framework reveals that system-level optimization of the pretreatment process effectively reduces mean particle size, narrows size distribution, and increases specific surface area. By systematically evaluating the operating space, an optimal window that yields a 2.97% improvement in glucose conversion is identified. This work provides practical guidance and a generalizable framework for optimizing mechanical pretreatment to maximize target product yields.

Ensuring the reliability of industrial machinery under evolving operating conditions is a critical engineering challenge. While deep learning facilitates status monitoring, traditional static models often suffer from catastrophic forgetting when adapting to dynamic environments. Furthermore, standard rehearsal strategies frequently introduce severe data imbalance, causing models to bias heavily toward novel fault categories at the expense of historical knowledge, thereby undermining diagnostic accuracy over time. To address these issues, this paper proposes a lifelong learning framework integrating Gaussian mixture model-based feature augmentation with projection distillation (GMMP). GMMP synthesizes representative pseudo-features through a Gaussian mixture-based local augmentation mechanism centered on historical class prototypes to enhance replay diversity for previously learned categories. Concurrently, a projection distillation mechanism aligns current and historical model representations within a normalized subspace by minimizing angular discrepancies, preserving critical prior knowledge while mitigating feature drift. Additionally, a dynamic weighting mechanism orchestrates the training process to rigorously balance stability and plasticity. Evaluated on two bearing datasets, GMMP demonstrates strong performance in retaining historical knowledge while adapting to new fault types, consistently outperforming state-of-the-art baselines. Visualization and quantitative comparisons confirm that our approach effectively alleviates catastrophic forgetting while preserving highly discriminative feature representations. The proposed method offers a robust solution for sustainable equipment maintenance in dynamic industrial environments.

The sustainable expansion of hydrogen supply system within the small-scale chemical industrial park is currently bottlenecked by the dual challenges of prohibitive labour costs associated with replacement of depleted hydrogen tanks and the safety risks inherent to process upset or reaction runaway in hydrogen production equipments. Addressing these operational constraints calls for the development of a shared hydrogen supply system, in which infrastructure layout and safety objectives are generally stepwise optimized. To this end, this paper proposes a novel design methodology through integrating a superstructure-based network design with a transshipment model, capturing the dynamic interaction between exchange hydrogen flow and hydrogen supply. The superstructure enumerates all feasible exchange routes among hydrogen tanks and production equipment, while the transshipment formulation identifies the cost-minimizing flow allocation and facility locations. This integration captures the dynamic interaction between hydrogen flow exchange and facility layout within a single framework, which has not been achieved in existing shared hydrogen system designs. Based on a multi-period mixed-integer nonlinear programming (MINLP) formulation, this framework explicitly incorporates a multi-branch connected hydrogen network and quantifies safety risks into quantitative layout constraints, thereby achieving a robust trade-off between economic efficiency and industrial park safety. In the case study, the proposed method reduced the total operational cost by 12.5

Signals and Systems serves as a core professional foundation course for postgraduates in energy and power engineering, bridging mathematical theory with engineering applications such as intelligent monitoring, fault diagnosis, and dynamic control of energy and power systems. It is a pivotal course for cultivating digital and intelligent competencies among energy and power professionals. Addressing prevalent issues in current teaching—including the disjunction between theoretical instruction and industry practice, rigid teaching models, theory-biased assessment, and insufficient professional adaptability—this study proposes targeted reform pathways across five dimensions: teaching objective reconstruction, teaching content optimization, teaching model innovation, assessment system reform, and curriculum ideological and political integration. Drawing on teaching practice outcomes and supported by educational research literature, this paper aims to construct a specialized Signals and Systems teaching system tailored to energy and power engineering, enhance students’ signal analysis and engineering practice capabilities, and facilitate the cultivation of high-quality innovative talents in the energy and power sector.

In recent years, substantial progress has been achieved in addressing cross-domain fault diagnosis problems through knowledge transfer strategies. However, traditional studies usually assume that target data are provided to facilitate domain adaptation throughout the training process, which is often infeasible in practical engineering applications, where fault data are often scarce. Therefore, extracting general and effective diagnostic leveraged knowledge from the source domain and applying it to unknown target tasks are crucial for intelligent cross-domain fault detection. To this end, this paper introduces a curvature-sensitive regulation strategy (CSRS) for cross-domain generalization of domain-invariant features in bearing fault diagnosis. To ensure high diagnostic accuracy in previously unobserved domains, this study proposes a model that converges to a smooth region with low loss values, allowing for the extraction of domain-invariant features, distinguishing features from source domains that may differ, which can be maintained in new target domains without requiring the presence of fault data. The effectiveness, superiority, and robustness of the CSRS method have been confirmed in generalization tasks across various fault diagnosis domains.

In recent years, the fault diagnosis method based on continuous learning has been widely used in the field of health monitoring of key components of rotating machinery. Most existing continuous learning fault diagnosis methods are deployed in an environment of relatively balanced datasets. However, the fault data generated by mechanical equipment under actual working conditions often have a long-tailed distribution, and the lack of tail fault data worsens the catastrophic forgetting problem of traditional continuous learning methods. A new prototype space boundary alignment network (PSBAN) for bearing continuous fault diagnosis under class incremental scenarios is proposed to solve the above problems. First, in the first stage, a new fusion loss function is designed to make the extracted features more robust. Then, in the second stage, the cluster mean algorithm is used to construct the prototype space of the class. At the same time, a total variance method is proposed to accurately estimate the central trend of the class and thus prevent the overdependence on the tail class from making the variance estimation inaccurate. Finally, the boundary of the prototype space is aligned by using the total variance. Experiments validate that the proposed PSBAN has higher accuracy than other methods do and effectively alleviates the catastrophic forgetting problem under the long-tailed distribution.

In the weak star simulator, the background stray light of the self-excited star image and the simulated target star light are mixed with each other, which is difficult to separate and reduces the simulation accuracy of the star position. Therefore, based on the polarized light tracing method, this paper explores the induction mechanism of the polarization effect of the weak star simulator on the background stray light field of the star map, and proposes a polarization balance optimization method for the beam splitter film. At the same time, the mapping model of star position simulation accuracy, polarization parameters of beam splitter and polarization stray light suppression degree is established. Based on this evaluation function, the polarization balance optimization process of beam splitter is constructed to reduce the influence of polarization effect of beam splitter on the background stray light field of star image. The simulation and experimental results show that the stray light suppression ability of the weak star simulator is improved by 2.1 times and the simulation accuracy of the star position is improved by 1.64 times after the polarization balance optimization of the beam splitter film.

Rotating machinery may constantly generate new classes of faults in complex operating environments, with a finite set of fault samples that are obtainable. The incremental nature of fault types and the scarcity of samples present great challenges for fault diagnosis models employing deep learning techniques, such as overfitting due to insufficient data and stability–plasticity problem when new fault samples are introduced. A few-shot class-incremental learning (FSCIL) method with adjustable pseudo-incremental sessions (APIS) is introduced in this study to overcome the abovementioned concerns. First, the method extracts fault samples from the sufficient data of the real base session, and it sets the pseudo FSCIL task according to the data format of FSCIL. The pseudo FSCIL task is employed to extract invariant features in the base and incremental session, and the feature space is trained to provide a reference model for the real fault incremental task. Second, the adjuster based on a self-attention mechanism is used to learn more discriminative features. Feature differences are observed between the old fault classifier and a fresh fault prototype. Based on the self-attention mechanism, the distinguishing features between the fault samples in the testing dataset and the old classifier are highlighted. In this way, a fault diagnosis model of the few-shot fault class increment with more generalization ability is acquired. A case study shows that APIS can effectively alleviate stability–plasticity and overfitting problems when dealing with FSCIL bearing fault diagnosis tasks.

In March 2020, the Central Committee of the Communist Party of China and the State Council issued the Opinions on Comprehensively Strengthening Labor Education in Primary and Secondary Schools in the New Era, emphasizing that "fully understand the new requirements for strengthening labor education in the new era to train socialist builders and successors; Comprehensively build a labor education system that reflects the characteristics of The Times; Carry out labor education practice extensively; Efforts to improve labor education support and security ability; Effectively strengthen the organization and implementation of labor education." Therefore, college students' labor education has become a compulsory course for the major of measurement and control technology and instrument. The course is divided into two parts: theoretical study and practical study. In view of the increasing emphasis of the society on college students' labor quality education and the key role of mapping in solving complex engineering problems, the traditional "passing through" theoretical teaching methods and "visiting" practical teaching methods are no longer suitable for the new teaching needs in the contemporary background. Therefore, in order to improve the teaching quality and students' learning effect on this malleable and open knowledge, Changzhou Institute of Technology invited technical directors of representative enterprises in the industry to conduct classroom case analysis and field visits of relevant knowledge, so as to realize the teaching reform of production-education integration in school-enterprise joint teaching. The latest industry status and development trend of measurement and control technology and instrument technology will be brought into the classroom teaching, effectively improve the teaching quality, and deeply cultivate students' labor consciousness.

In March 2020, the Central Committee of the Communist Party of China and the State Council issued the "Opinions on Strengthening Labor Education in Colleges and Universities in the New Era," emphasizing the "need to fully recognize the new requirements for cultivating socialist builders and successors in the new era for strengthening labor education; to build a comprehensive labor education system that reflects the times; to conduct extensive labor education practical activities; to enhance the support and guarantee capacity for labor education; and to strengthen the organization and implementation of labor education." As a result, labor education has become a required course for students majoring in Test and Control Technology and Instrumentation. The course is divided into two parts: theoretical learning and practical learning. Due to the increasing social emphasis on labor quality education and its reflection in the key role it plays in solving complex engineering problems, the traditional "walking-horse-view" theoretical teaching method and "visiting-style" practical teaching method are no longer suitable for the new teaching needs in the contemporary context. Therefore, in order to improve the teaching quality and students' learning effect of the flexible and open knowledge, Changzhou Institute of Technology's Test and Control Technology and Instrumentation major has invited the technical directors of representative enterprises in the industry to conduct case analysis in class and on-site visits, achieving the goal of joint teaching reform between schools and enterprises. Bringing the latest industrial status and development trends of Test and Control Technology and Instrumentation technology into classroom teaching, it effectively improves the teaching quality and deeply cultivates students' labor awareness.

This study presents a novel Bayesian-K Nearest Neighbor (B-KNN) fusion algorithm aimed at mitigating dynamic positioning errors in navigation systems that utilize visible light communication. The proposed fusion methodology leverages a fingerprint database constructed from the received signal strength data acquired by a mobile receiver. Through the selection of the nearest navigation path points for clustering and the utilization of a Bayesian algorithm with Gaussian fitting, the posterior probability of each cluster is computed to facilitate the prediction of the location of navigation path points via fingerprint matching. The results indicate that 89.8% of simulations exhibit a dynamic positioning error below 1 m within a 20 mx5 mx4 m model, achieving a success rate of 89.8%. Comparative analysis reveals that the B-KNN fusion algorithm enhances dynamic positioning accuracy by 68.3% while reducing computational overhead by approximately 48.3% in contrast to conventional KNN algorithms. Experimental assessments demonstrate that in scenarios involving ambient light interference within a 2 mx1 mx1.5 m model, the majority (95%) of dynamic positioning errors exceed 9 cm, with a significant portion surpassing 10 cm. Conversely, under conditions devoid of ambient light interference, only 4% of errors exceed 9 cm, with 14% falling below 5 cm, indicative of a marked 39.81% improvement in accuracy.

Modern quality management is one of the most extended courses in the quality course cluster, the core curriculum of the specialties of measurement and control technology and instrumentation. The course is divided into two main parts: the ISO 9000 standard and the ISO 9001 standard. In view of the growing importance of quality management systems in society and the key role that quality management plays in solving complex engineering problems, the traditional purely theoretical teaching methods are no longer suitable for the new demands of teaching in the contemporary context. Therefore, in order to improve the quality of teaching and the students' learning effect of this extended and open knowledge, Changzhou Institute of Technology invited technical supervisors from representative enterprises in the industry, such as Changzhou Wuji Management Consulting Company Limited, to conduct classroom case studies on relevant knowledge and to realize the reform of industry-education integration through joint teaching by the university and enterprises. The latest industrial status and development trend of measurement and control technology and instrumentation technology are brought into the classroom teaching, effectively improving the teaching quality and deeply cultivating students' awareness of modern quality management.

为了提高现行室内可见光定位系统的定位精度,提出考虑噪声干扰的动态惯性权重及认知因素的改进型粒子群算法.首先,将决定定位精度的欧式距离转换为 目标函数最小值优化问题;其次,利用惯性权重动态赋值,增强粒子群算法初期的全局搜索能力和后期的局部搜索能力;然后,利用正弦函数使得个体认知因素值非线性地减小,利用余弦函数使得群体认知因素值线性地增加,以进一步提升定位精度;最后,通过仿真与实验测试对所提定位算法进行验证.仿真测试结果表明,在5m×5m×3m和5m×4m×3m两种定位模型中,在0,0.5,1.0和1.5 m四个高度平面的空间定位平均误差分别为0.65和0.54 cm;实验结果显示,在搭建的1m×1m×0.8m和1 m×0.8 m×0.8 m室内空间中的平均定位误差分别为2.67和1.81 cm.

Magnetic microspheres, charged soot and other charged sub-micron particles are widely used, and the accurate measurement of their particle size is crucial to biomedical, environmental monitoring and other fields. During light scattering measurements, the light energy distribution projected onto each ring sur-face of the photodetector is the core data for particle size inversion calculations. When the measured par-ticle is charged, the light energy distribution will be different from its neutral state due to the scattering -enhancing effect of surface charges. Therefore, it is necessary to confirm the extent of the scattering -enhancing effect on the light energy distribution by numerical simulation, before actual measurement. In this paper, calculation models of scattered light energy distribution are established for charged sin-gle particle, monodisperse particle system and polydisperse particle system respectively. Considering the difference between the complex refractive indices of charged particles, the forward scattering of silica and polystyrene with different monodisperse degrees are taken as an example to analyze the light en-ergy distribution. The results show that the scattering-enhancing effect of surface charges can affect the light energy distribution of submicron particles, and the smaller the median diameter of the particle sys-tem and the lower the monodisperse degree, the more significant the effect is. At a median diameter of 0.1 mu m, the light energy of charged particles with different monodisperse degrees increased by an aver-age of about 3.5 times, near the scattering angle of 45 degrees This research realizes the forward deduction of the light energy distribution for charged particles, and provides the necessary theoretical support for the visible light measurement of charged submicron and nanoparticles.(c) 2023 Elsevier Ltd. All rights reserved.

Improving mass transfer in gas diffusion layers is critical to achieving high-performance proton-exchange membrane fuel cells (PEMFCs). Leaks through the interface between the gas and the membrane electrode assembly frame have been widely investigated, and the controllability of the cathode gas diffusion has not been achieved in most studies. In this study, we develop a structural parameter to investigate the controllability of the gas diffusion mechanism in the cathode in order to improve upon the design and performance of PEMFCs. This parameter accounts for the cathode gas diffusion layer porosity and carbon loading inside the catalyst layer. It is comprehensively calculated to relax the two segments’ distribution along three directions of the coordinate axis. The experimental and simulation results show that the obtained values of the parameter vary and change during voltage stabilization. According to the results, regardless of the materials in the cathode gas diffusion layer, the same steady-state voltage is obtained when the parameter is fixed. The cell could be controllably operated for a wide range of diffusion layer thicknesses by selecting the optimal parameter.

This major is guided by the principle of "application first, strengthening engineering practice ability, integrating industry and education, highlighting the background of optoelectronic industry, combining industry and management, and showing quality management characteristics". It is dedicated to cultivating application-oriented engineering talents who can meet the needs of regional optoelectronic industry and have the ability to design, implement and apply quality control systems for measurement and control systems and instrumentation production processes, and can engage in technology development, engineering application, production operation, quality management and other work in instrumentation and related fields. The orientation of the major is clear, and the service orientation is clear, which is in line with the development orientation and school direction of the university. Curriculum ideological and political teaching reform for this professional course has the unparalleled advantages of traditional science and technology courses. The education in humanistic cultivation, good moral development and solving complex engineering problems is to explore the resources of curriculum ideology and politics in the curriculum teaching.

In order to further improve the positioning accuracy of the indoor visible light positioning system, an improved particle swarm optimization (IPSO) algorithm is proposed. Considering the influence of noise, the positioning problem is transformed into the minimum distance evaluation problem. In the initial stage of the algorithm search, in order to enhance the global search ability of the algorithm, the sine function is used to increase the inertial weight value. In the later stage of the algorithm search, in order to enhance the local search ability of the algorithm, the cosine function is used to rapidly reduce the inertial weight value. At the same time, Gaussian limited density function is added to further improve the positioning accuracy of the algorithm. Finally, the positioning effectiveness of the proposed algorithm is verified through simulation and actual positioning experiments. The results show that, in the simulation phase, 95.2% of the positioning accuracy of the 5 m x 5 m x 3 m positioning model is less than 3 cm, and the positioning error in the space below 2.2 m is less than 3 cm. The system overhead ranges from 0.12 s to 0.27 s, and the average system overhead is 0.19 s. In the experimental test stage, 92.78% of the positioning accuracy is less than 4 cm in the 1 m x 1 m x 1 m positioning space, and 9.44% of the positioning accuracy reaches the millimeter level.

The accuracy of star position simulation in star simulation systems is a key factor affecting the accuracy of ground calibration experiments for star sensors. In order to solve the problem that the current simulation star position accuracy is restricted by the size of its constituent elements, In this paper, the mathematical model for simulating the star position correction and the gray distribution of the constituent elements is derived, and the functional representation of the azimuth and longitude correction, elevation and latitude correction of the simulated star position is established. Exploring the distribution law of star position simulation error and establishing a corrected sub interval based on it, achieving sub pixel level star position simulation accuracy. The experimental results show that after using the method proposed in this article, the accuracy of star position simulation has been improved by 1.74 times.

The deepening of industry education integration and school enterprise cooperation has put forward higher requirements for the teaching of professional basic courses in applied universities. This paper focuses on the compulsory course "Error Theory and Data Processing" for measurement and control majors, identifies the gap between current course teaching and industry demand, explores the method of curriculum construction for industry integration based on the current teaching situation and existing problems, clarifies the teaching reform goal of deep integration of courses and majors, proposes teaching reform measures suitable for measurement and control majors in applied universities, and solves the problem of insufficient integration of course teaching and professional industry.